Evidence map›Paper›PMID 37693397›Full record

ArticlebioRxiv : the preprint server for biology2023

Convolutional neural network models of neuronal responses in macaque V1 reveal limited non-linear processing.

Hui-Yuan Miao, Frank Tong

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Hui-Yuan MiaoDepartment of Psychology, Vanderbilt University, Nashville, TN, 37240, USA.
Frank TongDepartment of Psychology, Vanderbilt University, Nashville, TN, 37240, USA.

Funding

Shop Module CoreP30EY008126 · NEI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Kevin L Schey · 1989 to 2026
$19.6M
Neural and computational mechanisms underlying robust object recognitionR01EY035157 · NEI · VANDERBILT UNIVERSITY · PI FRANK TONG · 2023 to 2026
$1.6M
Perceptual functions of the human lateral geniculate nucleusR01EY029278 · NEI · VANDERBILT UNIVERSITY · PI TONG, FRANK · 2018 to 2021
$1.5M
NEI NIH HHS P30 EY008126NEI NIH HHS R01 EY029278NEI NIH HHS R01 EY035157
6 · The paper itself

Abstract

Computational models of the primary visual cortex (V1) have suggested that V1 neurons behave like Gabor filters followed by simple non-linearities. However, recent work employing convolutional neural network (CNN) models has suggested that V1 relies on far more non-linear computations than previously thought. Specifically, unit responses in an intermediate layer of VGG-19 were found to best predict macaque V1 responses to thousands of natural and synthetic images. Here, we evaluated the hypothesis that the poor performance of lower-layer units in VGG-19 might be attributable to their small receptive field size rather than to their lack of complexity

Indexed as

computational modelingdeep neural networksGabor V1 modelmacaquePrimary visual cortex

Identifiers

PMID37693397
PMCPMC10491131

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.